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Hot Papers 2021-01-21

1. Introduction to Normalizing Flows for Lattice Field Theory

Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a concrete implementation of the framework is presented. We apply this framework to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding gauge symmetries in the flow-based approach to the latter. This presentation is intended to be interactive and working with the attached Jupyter notebook is recommended.

2. No More Handshaking: How have COVID-19 pushed the expansion of computer-mediated communication in Japanese idol culture?

Hiromu Yakura

  • retweets: 1280, favorites: 114 (01/22/2021 10:56:36)
  • links: abs | pdf
  • cs.HC

In Japanese idol culture, meet-and-greet events where fans were allowed to handshake with an idol member for several seconds were regarded as its essential component until the spread of COVID-19. Now, idol groups are struggling in the transition of such events to computer-mediated communication because these events had emphasized meeting face-to-face over communicating, as we can infer from their length of time. I anticipated that investigating this emerging transition would provide implications because their communication has a unique characteristic that is distinct from well-studied situations, such as workplace communication and intimate relationships. Therefore, I first conducted a quantitative survey to develop a precise understanding of the transition, and based on its results, had semi-structured interviews with idol fans about their perceptions of the transition. The survey revealed distinctive approaches, including one where fans gathered at a venue but were isolated from the idol member by an acrylic plate and talked via a video call. Then the interviews not only provided answers to why such an approach would be reasonable but also suggested the existence of a large gap between conventional offline events and emerging online events in their perceptions. Based on the results, I discussed how we can develop interaction techniques to support this transition and how we can apply it to other situations outside idol culture, such as computer-mediated performing arts.

3. SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices

Xin Liu, Yuang Li, Josh Fromm, Yuntao Wang, Ziheng Jiang, Alex Mariakakis, Shwetak Patel

  • retweets: 342, favorites: 69 (01/22/2021 10:56:36)
  • links: abs | pdf
  • cs.HC | cs.CV

Super-resolution (SR) is a coveted image processing technique for mobile apps ranging from the basic camera apps to mobile health. Existing SR algorithms rely on deep learning models with significant memory requirements, so they have yet to be deployed on mobile devices and instead operate in the cloud to achieve feasible inference time. This shortcoming prevents existing SR methods from being used in applications that require near real-time latency. In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock. The SplitSRBlock supports channel-splitting, allowing the residual blocks to retain spatial information while reducing the computation in the channel dimension. SplitSR has a hybrid design consisting of standard convolutional blocks and lightweight residual blocks, allowing people to tune SplitSR for their computational budget. We evaluate our system on a low-end ARM CPU, demonstrating both higher accuracy and up to 5 times faster inference than previous approaches. We then deploy our model onto a smartphone in an app called ZoomSR to demonstrate the first-ever instance of on-device, deep learning-based SR. We conducted a user study with 15 participants to have them assess the perceived quality of images that were post-processed by SplitSR. Relative to bilinear interpolation — the existing standard for on-device SR — participants showed a statistically significant preference when looking at both images (Z=-9.270, p<0.01) and text (Z=-6.486, p<0.01).

4. Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation

Nitin Agrawal, Reuben Binns, Max Van Kleek, Kim Laine, Nigel Shadbolt

  • retweets: 102, favorites: 36 (01/22/2021 10:56:36)
  • links: abs | pdf
  • cs.HC

Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis. Due to their relative novelty, complexity, and opacity, these technologies provoke a variety of novel questions for design and governance. We interviewed researchers, developers, industry leaders, policymakers, and designers involved in their deployment to explore motivations, expectations, perceived opportunities and barriers to adoption. This provided insight into several pertinent challenges facing the adoption of these technologies, including: how they might make a nebulous concept like privacy computationally tractable; how to make them more usable by developers; and how they could be explained and made accountable to stakeholders and wider society. We conclude with implications for the development, deployment, and responsible governance of these privacy-preserving computation techniques.

5. VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

Anton Abilov, Yiqing Hua, Hana Matatov, Ofra Amir, Mor Naaman

  • retweets: 40, favorites: 58 (01/22/2021 10:56:36)
  • links: abs | pdf
  • cs.SI

The wide spread of unfounded election fraud claims surrounding the U.S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U.S. capitol. Under these circumstances, it is critical to understand discussions surrounding these claims on Twitter, a major platform where the claims disseminate. To this end, we collected and release the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and 25.6M retweets from 2.6M users related to voter fraud claims. To make this data immediately useful for a wide area of researchers, we further enhance the data with cluster labels computed from the retweet graph, user suspension status, and perceptual hashes of tweeted images. We also include in the dataset aggregated information for all external links and YouTube videos that appear in the tweets. Preliminary analyses of the data show that Twitter’s ban actions mostly affected a specific community of voter fraud claim promoters, and exposes the most common URLs, images and YouTube videos shared in the data.

6. UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang

  • retweets: 46, favorites: 22 (01/22/2021 10:56:37)
  • links: abs | pdf
  • cs.LG | cs.AI

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs 6 multi-agent games). In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task’s decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves significant results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10 times faster).

7. Epidemic? The Attack Surface of German Hospitals during the COVID-19 Pandemic

Johannes Klick, Robert Koch, Thomas Brandstetter

  • retweets: 42, favorites: 8 (01/22/2021 10:56:37)
  • links: abs | pdf
  • cs.CR | cs.CY

In our paper we analyze the attack surface of German hospitals and healthcare providers in 2020 during the COVID-19 Pandemic. The analysis looked at the publicly visible attack surface utilizing a Distributed Cyber Recon System, utilizing distributed Internet scanning, Big Data methods and scan data of 1,483 GB from more than 89 different global Internet scans. From the 1,555 identified German clinical entities, security posture analysis was conducted by looking at more than 13,000 service banners for version identification and subsequent CVE-based vulnerability identification. Primary analysis shows that 32 percent of the analyzed services were determined as vulnerable to various degrees and 36 percent of all hospitals showed numerous vulnerabilities. Further resulting vulnerability statistics were mapped against size of organization and hospital bed count.

Published 22 Jan 2021

ML Lead at Beatrust. (https://beatrust.com)Tatsuya Shirakawa on Twitter